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Estimating cyber attack risk from healthcare employee behaviour using a HEXACO machine learning model

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  • Kenneth David Strang

Abstract

Cyber attack risk is examined by collecting a sample from healthcare business employees using the previously validated six-factor HEXACO personality theory construct from the psychology discipline. Cybercrime theories and studies are reviewed from sociology, criminology and computer science. The research design involved developing a predictive logistic regression model using machine learning. Control variables were added to capture fixed participant demographics. The result was a significant model with 95% classification accuracy, and a 60% McFadden effect size. Two of the six HEXCACO factors predicted cyber attack risk: humility and openness, while none of the control variables had any impact.

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  • Kenneth David Strang, 2025. "Estimating cyber attack risk from healthcare employee behaviour using a HEXACO machine learning model," International Journal of Business Continuity and Risk Management, Inderscience Enterprises Ltd, vol. 15(3), pages 234-262.
    • Handle: RePEc:ids:ijbcrm:v:15:y:2025:i:3:p:234-262
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